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  1. Article ; Online: Digital Phenotyping in Clinical Neurology.

    Gupta, Anoopum S

    Seminars in neurology

    2022  Volume 42, Issue 1, Page(s) 48–59

    Abstract: Internet-connected devices, including personal computers, smartphones, smartwatches, and voice assistants, have evolved into powerful multisensor technologies that billions of people interact with daily to connect with friends and colleagues, access and ... ...

    Abstract Internet-connected devices, including personal computers, smartphones, smartwatches, and voice assistants, have evolved into powerful multisensor technologies that billions of people interact with daily to connect with friends and colleagues, access and share information, purchase goods, play games, and navigate their environment. Digital phenotyping taps into the data streams captured by these devices to characterize and understand health and disease. The purpose of this article is to summarize opportunities for digital phenotyping in neurology, review studies using everyday technologies to obtain motor and cognitive information, and provide a perspective on how neurologists can embrace and accelerate progress in this emerging field.
    MeSH term(s) Humans ; Neurologists ; Neurology ; Smartphone
    Language English
    Publishing date 2022-01-11
    Publishing country United States
    Document type Journal Article ; Research Support, N.I.H., Extramural ; Research Support, Non-U.S. Gov't
    ZDB-ID 603165-1
    ISSN 1098-9021 ; 0271-8235
    ISSN (online) 1098-9021
    ISSN 0271-8235
    DOI 10.1055/s-0041-1741495
    Database MEDical Literature Analysis and Retrieval System OnLINE

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    In stock of ZB MED Cologne/Königswinter

    Zs.A 1737: Show issues Location:
    Je nach Verfügbarkeit (siehe Angabe bei Bestand)
    bis Jg. 1994: Bestellungen von Artikeln über das Online-Bestellformular
    Jg. 1995 - 2021: Lesesall (1.OG)
    ab Jg. 2022: Lesesaal (EG)

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  2. Article: Digital Phenotyping in Clinical Neurology

    Gupta, Anoopum S.

    Seminars in Neurology

    (Tele-Neurology)

    2022  Volume 42, Issue 01, Page(s) 48–59

    Abstract: Internet-connected devices, including personal computers, smartphones, smartwatches, and voice assistants, have evolved into powerful multisensor technologies that billions of people interact with daily to connect with friends and colleagues, access and ... ...

    Series title Tele-Neurology
    Abstract Internet-connected devices, including personal computers, smartphones, smartwatches, and voice assistants, have evolved into powerful multisensor technologies that billions of people interact with daily to connect with friends and colleagues, access and share information, purchase goods, play games, and navigate their environment. Digital phenotyping taps into the data streams captured by these devices to characterize and understand health and disease. The purpose of this article is to summarize opportunities for digital phenotyping in neurology, review studies using everyday technologies to obtain motor and cognitive information, and provide a perspective on how neurologists can embrace and accelerate progress in this emerging field.
    Keywords digital phenotyping ; quantitative phenotyping ; biomarkers ; outcome measures
    Language English
    Publishing date 2022-01-11
    Publisher Thieme Medical Publishers, Inc.
    Publishing place Stuttgart ; New York
    Document type Article
    ZDB-ID 603165-1
    ISSN 1098-9021 ; 0271-8235
    ISSN (online) 1098-9021
    ISSN 0271-8235
    DOI 10.1055/s-0041-1741495
    Database Thieme publisher's database

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    In stock of ZB MED Cologne/Königswinter

    Zs.A 1737: Show issues Location:
    Je nach Verfügbarkeit (siehe Angabe bei Bestand)
    bis Jg. 1994: Bestellungen von Artikeln über das Online-Bestellformular
    Jg. 1995 - 2021: Lesesall (1.OG)
    ab Jg. 2022: Lesesaal (EG)

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  3. Article ; Online: Assessing Cerebellar Disorders with Wearable Inertial Sensor Data Using Time-Frequency and Autoregressive Hidden Markov Model Approaches.

    Knudson, Karin C / Gupta, Anoopum S

    Sensors (Basel, Switzerland)

    2022  Volume 22, Issue 23

    Abstract: Wearable sensor data is relatively easily collected and provides direct measurements of movement that can be used to develop useful behavioral biomarkers. Sensitive and specific behavioral biomarkers for neurodegenerative diseases are critical to ... ...

    Abstract Wearable sensor data is relatively easily collected and provides direct measurements of movement that can be used to develop useful behavioral biomarkers. Sensitive and specific behavioral biomarkers for neurodegenerative diseases are critical to supporting early detection, drug development efforts, and targeted treatments. In this paper, we use autoregressive hidden Markov models and a time-frequency approach to create meaningful quantitative descriptions of behavioral characteristics of cerebellar ataxias from wearable inertial sensor data gathered during movement. We create a flexible and descriptive set of features derived from accelerometer and gyroscope data collected from wearable sensors worn while participants perform clinical assessment tasks, and use these data to estimate disease status and severity. A short period of data collection (<5 min) yields enough information to effectively separate patients with ataxia from healthy controls with very high accuracy, to separate ataxia from other neurodegenerative diseases such as Parkinson’s disease, and to provide estimates of disease severity.
    MeSH term(s) Humans ; Wearable Electronic Devices ; Movement ; Parkinson Disease/diagnosis ; Cerebellar Diseases ; Ataxia
    Language English
    Publishing date 2022-12-03
    Publishing country Switzerland
    Document type Journal Article
    ZDB-ID 2052857-7
    ISSN 1424-8220 ; 1424-8220
    ISSN (online) 1424-8220
    ISSN 1424-8220
    DOI 10.3390/s22239454
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  4. Article ; Online: Rates of change of pons and middle cerebellar peduncle diameters are diagnostic of multiple system atrophy of the cerebellar type.

    Stephen, Christopher D / Vangel, Mark / Gupta, Anoopum S / MacMore, Jason P / Schmahmann, Jeremy D

    Brain communications

    2024  Volume 6, Issue 1, Page(s) fcae019

    Abstract: Definitive diagnosis of multiple system atrophy of the cerebellar type (MSA-C) is challenging. We hypothesized that rates of change of pons and middle cerebellar peduncle diameters on MRI would be unique to MSA-C and serve as diagnostic biomarkers. We ... ...

    Abstract Definitive diagnosis of multiple system atrophy of the cerebellar type (MSA-C) is challenging. We hypothesized that rates of change of pons and middle cerebellar peduncle diameters on MRI would be unique to MSA-C and serve as diagnostic biomarkers. We defined the normative data for anterior-posterior pons and transverse middle cerebellar peduncle diameters on brain MRI in healthy controls, performed diameter-volume correlations and measured intra- and inter-rater reliability. We studied an Exploratory cohort (2002-2014) of 88 MSA-C and 78 other cerebellar ataxia patients, and a Validation cohort (2015-2021) of 49 MSA-C, 13 multiple system atrophy of the parkinsonian type (MSA-P), 99 other cerebellar ataxia patients and 314 non-ataxia patients. We measured anterior-posterior pons and middle cerebellar peduncle diameters on baseline and subsequent MRIs, and correlated results with Brief Ataxia Rating Scale scores. We assessed midbrain:pons and middle cerebellar peduncle:pons ratios over time. The normative anterior-posterior pons diameter was 23.6 ± 1.6 mm, and middle cerebellar peduncle diameter 16.4 ± 1.4 mm. Pons diameter correlated with volume,
    Language English
    Publishing date 2024-02-21
    Publishing country England
    Document type Journal Article
    ISSN 2632-1297
    ISSN (online) 2632-1297
    DOI 10.1093/braincomms/fcae019
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  5. Article ; Online: At-home wearables and machine learning sensitively capture disease progression in amyotrophic lateral sclerosis.

    Gupta, Anoopum S / Patel, Siddharth / Premasiri, Alan / Vieira, Fernando

    Nature communications

    2023  Volume 14, Issue 1, Page(s) 5080

    Abstract: Amyotrophic lateral sclerosis causes degeneration of motor neurons, resulting in progressive muscle weakness and impairment in motor function. Promising drug development efforts have accelerated in amyotrophic lateral sclerosis, but are constrained by a ... ...

    Abstract Amyotrophic lateral sclerosis causes degeneration of motor neurons, resulting in progressive muscle weakness and impairment in motor function. Promising drug development efforts have accelerated in amyotrophic lateral sclerosis, but are constrained by a lack of objective, sensitive, and accessible outcome measures. Here we investigate the use of wearable sensors, worn on four limbs at home during natural behavior, to quantify motor function and disease progression in 376 individuals with amyotrophic lateral sclerosis. We use an analysis approach that automatically detects and characterizes submovements from passively collected accelerometer data and produces a machine-learned severity score for each limb that is independent of clinical ratings. We show that this approach produces scores that progress faster than the gold standard Amyotrophic Lateral Sclerosis Functional Rating Scale-Revised (-0.86 ± 0.70 SD/year versus -0.73 ± 0.74 SD/year), resulting in smaller clinical trial sample size estimates (N = 76 versus N = 121). This method offers an ecologically valid and scalable measure for potential use in amyotrophic lateral sclerosis trials and clinical care.
    MeSH term(s) Humans ; Amyotrophic Lateral Sclerosis/diagnosis ; Disease Progression ; Machine Learning ; Motor Neurons ; Wearable Electronic Devices
    Language English
    Publishing date 2023-08-21
    Publishing country England
    Document type Journal Article ; Research Support, N.I.H., Extramural
    ZDB-ID 2553671-0
    ISSN 2041-1723 ; 2041-1723
    ISSN (online) 2041-1723
    ISSN 2041-1723
    DOI 10.1038/s41467-023-40917-3
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  6. Article ; Online: Estimation of ataxia severity in children with ataxia-telangiectasia using ankle-worn sensors.

    Lee, Juhyeon / Oubre, Brandon / Daneault, Jean-Francois / Lee, Sunghoon Ivan / Gupta, Anoopum S

    Journal of neurology

    2023  Volume 270, Issue 10, Page(s) 5097–5101

    MeSH term(s) Humans ; Child ; Ataxia Telangiectasia/complications ; Ataxia Telangiectasia/diagnosis ; Ankle ; Ataxia ; Lower Extremity ; Ataxia Telangiectasia Mutated Proteins
    Chemical Substances Ataxia Telangiectasia Mutated Proteins (EC 2.7.11.1)
    Language English
    Publishing date 2023-06-27
    Publishing country Germany
    Document type Letter ; Research Support, N.I.H., Extramural
    ZDB-ID 187050-6
    ISSN 1432-1459 ; 0340-5354 ; 0012-1037 ; 0939-1517 ; 1619-800X
    ISSN (online) 1432-1459
    ISSN 0340-5354 ; 0012-1037 ; 0939-1517 ; 1619-800X
    DOI 10.1007/s00415-023-11786-z
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    In stock of ZB MED Cologne/Königswinter

    Ui II Zs.40: Show issues Location:
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    bis Jg. 2021: Bestellungen von Artikeln über das Online-Bestellformular
    ab Jg. 2022: Lesesaal (EG)

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  7. Book ; Online: Assessing Cerebellar Disorders With Wearable Inertial Sensor Data Using Time-Frequency and Autoregressive Hidden Markov Model Approaches

    Knudson, Karin C. / Gupta, Anoopum S.

    2021  

    Abstract: We use autoregressive hidden Markov models and a time-frequency approach to create meaningful quantitative descriptions of behavioral characteristics of cerebellar ataxias from wearable inertial sensor data gathered during movement. Wearable sensor data ... ...

    Abstract We use autoregressive hidden Markov models and a time-frequency approach to create meaningful quantitative descriptions of behavioral characteristics of cerebellar ataxias from wearable inertial sensor data gathered during movement. Wearable sensor data is relatively easily collected and provides direct measurements of movement that can be used to develop useful behavioral biomarkers. Sensitive and specific behavioral biomarkers for neurodegenerative diseases are critical to supporting early detection, drug development efforts, and targeted treatments. We create a flexible and descriptive set of features derived from accelerometer and gyroscope data collected from wearable sensors while participants perform clinical assessment tasks, and with them estimate disease status and severity. A short period of data collection ($<$ 5 minutes) yields enough information to effectively separate patients with ataxia from healthy controls with very high accuracy, to separate ataxia from other neurodegenerative diseases such as Parkinson's disease, and to give estimates of disease severity.

    Comment: 12 pages, 7 figures
    Keywords Quantitative Biology - Neurons and Cognition ; Statistics - Applications
    Subject code 310
    Publishing date 2021-08-19
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  8. Article ; Online: Free-Living Motor Activity Monitoring in Ataxia-Telangiectasia.

    Khan, Nergis C / Pandey, Vineet / Gajos, Krzysztof Z / Gupta, Anoopum S

    Cerebellum (London, England)

    2021  Volume 21, Issue 3, Page(s) 368–379

    Abstract: With disease-modifying approaches under evaluation in ataxia-telangiectasia and other ataxias, there is a need for objective and reliable biomarkers of free-living motor function. In this study, we test the hypothesis that metrics derived from a single ... ...

    Abstract With disease-modifying approaches under evaluation in ataxia-telangiectasia and other ataxias, there is a need for objective and reliable biomarkers of free-living motor function. In this study, we test the hypothesis that metrics derived from a single wrist sensor worn at home provide accurate, reliable, and interpretable information about neurological disease severity in children with A-T.A total of 15 children with A-T and 15 age- and sex-matched controls wore a sensor with a triaxial accelerometer on their dominant wrist for 1 week at home. Activity intensity measures, derived from the sensor data, were compared with in-person neurological evaluation on the Brief Ataxia Rating Scale (BARS) and performance on a validated computer mouse task.Children with A-T were inactive the same proportion of each day as controls but produced more low intensity movements (p < 0.01; Cohen's d = 1.48) and fewer high intensity movements (p < 0.001; Cohen's d = 1.71). The range of activity intensities was markedly reduced in A-T compared to controls (p < 0.0001; Cohen's d = 2.72). The activity metrics correlated strongly with arm, gait, and total clinical severity (r: 0.71-0.87; p < 0.0001), correlated with specific computer task motor features (r: 0.67-0.92; p < 0.01), demonstrated high reliability (r: 0.86-0.93; p < 0.00001), and were not significantly influenced by age in the healthy control group.Motor activity metrics from a single, inexpensive wrist sensor during free-living behavior provide accurate and reliable information about diagnosis, neurological disease severity, and motor performance. These low-burden measurements are applicable independent of ambulatory status and are potential digital behavioral biomarkers in A-T.
    MeSH term(s) Ataxia/diagnosis ; Ataxia Telangiectasia/diagnosis ; Gait ; Humans ; Motor Activity ; Reproducibility of Results
    Language English
    Publishing date 2021-07-24
    Publishing country United States
    Document type Journal Article
    ZDB-ID 2112586-7
    ISSN 1473-4230 ; 1473-4222
    ISSN (online) 1473-4230
    ISSN 1473-4222
    DOI 10.1007/s12311-021-01306-y
    Database MEDical Literature Analysis and Retrieval System OnLINE

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    Zs.A 5795: Show issues Location:
    Je nach Verfügbarkeit (siehe Angabe bei Bestand)
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    Jg. 1995 - 2021: Lesesall (2.OG)
    ab Jg. 2022: Lesesaal (EG)

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  9. Article: Automatic Classification and Severity Estimation of Ataxia From Finger Tapping Videos.

    Nunes, Adonay S / Kozhemiako, Nataliia / Stephen, Christopher D / Schmahmann, Jeremy D / Khan, Sheraz / Gupta, Anoopum S

    Frontiers in neurology

    2022  Volume 12, Page(s) 795258

    Abstract: Digital assessments enable objective measurements of ataxia severity and provide informative features that expand upon the information obtained during a clinical examination. In this study, we demonstrate the feasibility of using finger tapping videos to ...

    Abstract Digital assessments enable objective measurements of ataxia severity and provide informative features that expand upon the information obtained during a clinical examination. In this study, we demonstrate the feasibility of using finger tapping videos to distinguish participants with Ataxia (
    Language English
    Publishing date 2022-02-28
    Publishing country Switzerland
    Document type Journal Article
    ZDB-ID 2564214-5
    ISSN 1664-2295
    ISSN 1664-2295
    DOI 10.3389/fneur.2021.795258
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  10. Article: Detection of Oculomotor Dysmetria From Mobile Phone Video of the Horizontal Saccades Task Using Signal Processing and Machine Learning Approaches.

    Azami, Hamed / Chang, Zhuoqing / Arnold, Steven E / Sapiro, Guillermo / Gupta, Anoopum S

    IEEE access : practical innovations, open solutions

    2022  Volume 10, Page(s) 34022–34031

    Abstract: Eye movement assessments have the potential to help in diagnosis and tracking of neurological disorders. Cerebellar ataxias cause profound and characteristic abnormalities in smooth pursuit, saccades, and fixation. Oculomotor dysmetria (i.e., hypermetric ...

    Abstract Eye movement assessments have the potential to help in diagnosis and tracking of neurological disorders. Cerebellar ataxias cause profound and characteristic abnormalities in smooth pursuit, saccades, and fixation. Oculomotor dysmetria (i.e., hypermetric and hypometric saccades) is a common finding in individuals with cerebellar ataxia. In this study, we evaluated a scalable approach for detecting and quantifying oculomotor dysmetria. Eye movement data were extracted from iPhone video recordings of the horizontal saccade task (a standard clinical task in ataxia) and combined with signal processing and machine learning approaches to quantify saccade abnormalities. Entropy-based measures of eye movements during saccades were significantly different in 72 individuals with ataxia with dysmetria compared with 80 ataxia and Parkinson's participants without dysmetria. A template matching-based analysis demonstrated that saccadic eye movements in patients without dysmetria were more similar to the ideal template of saccades. A support vector machine was then used to train and test the ability of multiple signal processing features in combination to distinguish individuals with and without oculomotor dysmetria. The model achieved 78% accuracy (sensitivity= 80% and specificity= 76%). These results show that the combination of signal processing and machine learning approaches applied to iPhone video of saccades, allow for extraction of information pertaining to oculomotor dysmetria in ataxia. Overall, this inexpensive and scalable approach for capturing important oculomotor information may be a useful component of a screening tool for ataxia and could allow frequent at-home assessments of oculomotor function in natural history studies and clinical trials.
    Language English
    Publishing date 2022-03-04
    Publishing country United States
    Document type Journal Article
    ZDB-ID 2687964-5
    ISSN 2169-3536
    ISSN 2169-3536
    DOI 10.1109/access.2022.3156964
    Database MEDical Literature Analysis and Retrieval System OnLINE

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